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Technological innovation for agricultural statistics : key indicators for Asia and the Pacific 2018, special supplement.
| Clasificación: | Libro Electrónico |
|---|---|
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Manila, Philippines :
Asian Development Bank,
2018.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title; Copyright; Contents; Foreword; Abbreviations; Highlights; Introduction; Existing Methods for Collecting Agricultural and Rural Statistics in Asia and the Pacific; Administrative reporting systems; Sample surveys; Census of agriculture; Data Collection Activities in Project Areas; Measurement Error in Land Area, Yield, and Production Estimates; Land area; Production; Yield; Technology for Agricultural Statistics: A Potential Game-Changer; Remote sensing for land area measurement; Estimating rice yields from space; Remote sensing for developing a sampling frame
- Other Technological Innovations for Agricultural and Rural StatisticsDrones; Computer-assisted personal interviewing; Artificial intelligence; Conclusion; References; Figures; Figure 1: Land Area Measurement Bias, Self-Reported versus Global Positioning System (ha); Figure 2: Distribution of Differences in Plot Area; Figure 3: Production Measurement Bias, Self-Reported versus Objectively Measured; Figure 4: Distribution for Differences in Yield; Figure 5: Mapping Plot Boundaries Using Google Earth Images; Figure 6: Distribution for Differences in Area
- Figure 7: Normalized Difference Vegetation Index Time SeriesFigure 8: Classified Land Cover Map for Thai Binh Province, Viet Nam; Figure 9: Growth Cycle of Paddy Rice: A Conceptual Framework to Model Crop Yield; Figure 10: Plotting Time Series of Normalized Difference Vegetation Index Values; Figure 11: Linear Regression Model between the Peak of Vegetation Indices and Crop Yield; Figure 12: Spatially Explicit Yield Map Based on Normalized Difference Vegetation Index; Figure 13: Sample Mesh; Figure 14: Crop-Cutting on a Subplot
- Figure 15: Area Estimate Comparison from Remote Sensing and Official Statistics for Savannakhet, Lao People's Democratic RepublicFigure 16: Area Estimate Comparison from Remote Sensing and Official Statistics for Ang Thong, Thailand; Figure 17: Area Estimate Comparison from Remote Sensing and Official Statistics for Thai Binh, Viet Nam; Figure 18: Drone Image of Small-Scale Farms in Ghana; Figure 19: Drone Image of Small-Scale Farms in Uganda Indicating Crop Health; Figure 20: Computer-Assisted Personal Interviewing as Part of Project Activities
- Figure 21: Artificial Intelligence for Compiling Potential and Actual Crop YieldBoxes; Box 1: The Century Old Mystery of the Relationship Between Plot Size and Productivity for Small-Scale Farmers; Box 2: Land Area Measurement Using Survey Solutions; Box 3: Explaining Spatial and Temporal Resolution from a Remote Sensing Perspective; Table; Table 1: Distribution of Meshes in the Sampling Frame